Severe Local Storm: how HazardNet classifies and scores it

Squall wind plus a convective downpour.

How the model arrives at this class

Classified from the same drivers; Severe Local Storm and Tropical Cyclone compete directly on the wind term and are separated by the rainfall pattern and the other drivers.

Uncalibrated softmax, as above.

Nothing above the WATCH level can be published automatically while no calibration map exists, and nothing at all can be published while the pipeline stamps no model version — the alert engine records that as `publication_blocked` rather than issuing the alert.

The independent physics cross-check

Uses the peak 24-hour rainfall rather than the horizon total, precisely so a wet fortnight is not read as a squall line — the defect the corrected wiring removed.

Drivers: maximum wind in the horizon (W, km/h) · wettest 24 hours in that horizon (P_peak, mm).

clip(x) clamps a term to [0, 1] — max(0, min(1, x)) — which is how every formula in the pipeline bounds its terms. Each term is clipped before it is weighted, so no single driver can run away with a score.

  • Expression: 0.6*clip((W-50)/100) + 0.4*clip(P_peak/100)
  • That expression is executed against scripts/physics_severity.py by scripts/tests/test_content_engine.py — this page cannot silently describe a formula the pipeline no longer runs.

What the current run says about Severe Local Storm

The run this deployment ships (prediction date 2026-09-16) classifies no district-horizon unit as Severe Local Storm. An absence of severe local storm in one run is not a statement that the hazard cannot occur this season.

Season and geography

Typical season: March–May (Nor'wester / Kalbaishakhi), plus pre-monsoon squall lines. This describes when the hazard is climatologically plausible, not when this run flags it.

What this class cannot tell you

  • No convective-available-potential-energy, no storm-top height, no hail: the meteorology that defines a Nor'wester is not an input.
  • Peak wind in a district aggregate understates the local maximum that damages a bazaar.
  • Hail and lightning — the two things that actually kill in a Kalbaishakhi — are not represented at all.
  • Confidence bins (Certain ≥ 0.85, Probable 0.70–0.85, Uncertain < 0.70) describe the model's own certainty in the class it chose. They are not accuracy, and no accuracy figure is published because no held-out evaluation has been run on real labels.

Inputs behind it

Ground-truth reports (district, hazard, horizon, date, what was observed) are the only route by which this project can publish skill metrics. The contact page reaches the maintainers.

  • Open-Meteo forecast (wind, rainfall)
  • ERA5-Land daily aggregates
  • BMD bulletins (warnings, ingested as text)

Questions and answers

Is a high Severe Local Storm severity a prediction of damage?

No. The severity index is the model's continuous score for how strongly the drivers resemble this class, on a 0.00–1.00 scale. It is not a probability, not a percentage, and it does not model exposure. The physics cross-check is a second, independent estimate; where the two diverge, both numbers are shown rather than averaged.

Why is the confidence not a probability of Severe Local Storm?

The published confidence is the model's own softmax over its eight classes, labelled uncalibrated_model_softmax in the data. It says how certain the classifier is about the class it picked, not how often that class actually materialises. No calibration map has been fitted yet, which is also why nothing above the WATCH level can be published automatically.

Which districts does this page cover?

The district pages cover all 64 districts. This page summarises the districts the current run covers — a run can be partial, and the coverage stamp on the snapshot says exactly how partial, which the status page reports.

Content reviewed 2026-09-16. HazardNet is decision support, not an official warning service — see the methodology for scope and limitations.

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